Metric learning for image steganalysis

Image steganalysis based on supervised distance metric learning is to find an appropriate measure of similarity between image features where the distribution discrepancy between cover-images and stego-images are analyzed in the reduced dimensional space. Our approach is novel in that it combines the merits of weight metric learning and image distribution analysis in reduced dimension space. By this learning metrics, we exploit a new steganalysis metric to discriminate stego-images from clean images. The experiment results show the effectiveness of the propose approach for some data hiding method.